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PagedAttention

9 articles · Page 1 of 1
Tencent Technical Engineering
Tencent Technical Engineering
Sep 22, 2026 · Backend Development

Tencent Backend Veteran's Reset: Mastering LLM Inference Engineering from Zero

A Tencent senior backend engineer with 15 years experience shares his transition to LLM inference engineering, detailing learning paths for model architecture, inference optimization, and self-assessment exercises, while arguing that traditional infrastructure skills remain crucial for AI Infra and Agent Infra development.

AI infrastructureBackend EngineeringKV cache
0 likes · 38 min read
Tencent Backend Veteran's Reset: Mastering LLM Inference Engineering from Zero
Old Zhang's AI Learning
Old Zhang's AI Learning
Jun 7, 2026 · Artificial Intelligence

Hands‑On LLM Local Deployment: vLLM Inference Optimizations Explained

The article explains why LLM inference is memory‑bound, introduces vLLM’s three core optimizations—Continuous Batching, PagedAttention, and Prefix Caching—shows how to launch a vLLM server, run Python code to benchmark performance, and examines KV‑Cache memory usage with concrete numbers.

KV cacheLLM inferencePagedAttention
0 likes · 11 min read
Hands‑On LLM Local Deployment: vLLM Inference Optimizations Explained
DeepHub IMBA
DeepHub IMBA
Mar 3, 2026 · Artificial Intelligence

The Evolution of KV Cache Management: From Continuous Allocation to Unified Hybrid Memory Architecture

The article traces five eras of KV cache management for LLM inference—from its absence before Transformers to the emerging unified hybrid memory architecture—comparing vLLM, SGLang, and TensorRT‑LLM and offering a decision framework for selecting the right solution in various deployment scenarios.

KV cacheLLM inferenceMemory Management
0 likes · 16 min read
The Evolution of KV Cache Management: From Continuous Allocation to Unified Hybrid Memory Architecture
Ops Community
Ops Community
Jan 18, 2026 · Artificial Intelligence

How to Quadruple LLM Throughput with vLLM’s PagedAttention and Continuous Batching

This guide details how to replace native Transformers inference with the high‑performance vLLM engine, leveraging PagedAttention, continuous batching, tensor parallelism, and OpenAI‑compatible APIs to achieve 3‑4× higher throughput, lower latency, and scalable multi‑GPU deployments for production‑grade large language models.

GPU optimizationOpenAI API CompatibilityPagedAttention
0 likes · 61 min read
How to Quadruple LLM Throughput with vLLM’s PagedAttention and Continuous Batching
Infra Learning Club
Infra Learning Club
Apr 4, 2025 · Artificial Intelligence

Testing Augment Code: A Powerful New Rival to Cursor

The article evaluates Augment Code, an AI‑powered coding assistant with 200K token context, persistent memory, multimodal input, and top SWE‑bench scores, walks through its installation, explores its use on vllm and PagedAttention, demonstrates adding a new model and auto‑generating a WeChat mini‑program, and compares its capabilities and speed to Cursor.

AI coding assistantAugment CodeCursor
0 likes · 8 min read
Testing Augment Code: A Powerful New Rival to Cursor
Baobao Algorithm Notes
Baobao Algorithm Notes
Apr 5, 2024 · Artificial Intelligence

How vLLM’s PagedAttention Revolutionizes GPU Memory Management for LLM Inference

This article explains how vLLM’s PagedAttention, inspired by operating‑system virtual‑memory paging, dynamically allocates KV‑cache memory to dramatically reduce GPU memory fragmentation, improve throughput, and handle scheduling, preemption, and distributed inference for large language models.

GPU memoryLLM inferencePagedAttention
0 likes · 25 min read
How vLLM’s PagedAttention Revolutionizes GPU Memory Management for LLM Inference